A Graph-based Ant system and its convergence
Future Generation Computer Systems
Future Generation Computer Systems
Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
The Nonstochastic Multiarmed Bandit Problem
SIAM Journal on Computing
Ant Colony Optimization
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
Prediction, Learning, and Games
Prediction, Learning, and Games
On the runtime analysis of the 1-ANT ACO algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
The On-Line Shortest Path Problem Under Partial Monitoring
The Journal of Machine Learning Research
Information Processing Letters
First steps to the runtime complexity analysis of ant colony optimization
Computers and Operations Research
A survey on metaheuristics for stochastic combinatorial optimization
Natural Computing: an international journal
Running Time Analysis of ACO Systems for Shortest Path Problems
SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Ant colony optimization for stochastic shortest path problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Simple max-min ant systems and the optimization of linear pseudo-boolean functions
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Sharp bounds by probability-generating functions and variable drift
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Optimizing expected path lengths with ant colony optimization using fitness proportional update
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
Runtime analysis of ant colony optimization on dynamic shortest path problems
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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The first rigorous theoretical analysis (Horoba, Sudholt (GECCO 2010)) of an ant colony optimizer for the stochastic shortest path problem suggests that ant system experience significant difficulties when the input data is prone to noise. In this work, we propose a slightly different ant optimizer to deal with noise. We prove that under mild conditions, it finds the paths with shortest expected length efficiently, despite the fact that we do not have convergence in the classic sense. To prove our results, we introduce a stronger drift theorem that can also deal with the situation that the progress is faster when one is closer to the goal.